Salesforce AI Research: MCP-Universe benchmark for AI performance

Excited to share my latest Medium article — “The Hidden Engine of AI: A Deep Dive into MCP” ⚙️ In this piece, I uncover how the Model Context Protocol (MCP) is transforming AI models into powerful, connected systems — bridging tools, APIs, and data for smarter automation and seamless integration. If you’ve ever wondered how AI actually interacts with the real world — this is for you! 👉 Read here: https://lnkd.in/gqCBN2Wt #AI #MachineLearning #MCP #Technology #ArtificialIntelligence #Medium #Innovation

Is your AI silently degrading in production? Model drift is the invisible killer undermining your applications' performance. This practical guide dives deep into MLOps observability, equipping you with strategies to detect and manage AI model drift proactively, ensuring your systems remain reliable and accurate. Master AI resilience and prevent costly failures. Read the full guide now: https://lnkd.in/gKuaQ-2Q #MLOps #AI #ModelDrift #Observability #MachineLearning #DataScience

Missing piece of AI Puzzle - Ontology MIT study suggested 95% of the Enterprise AI initiatives didn’t fly to the expectation. They got Data of all kinds captured They got Data Lake as unified They got Foundational models of all kinds and size of tokens. Still, Failing to leverage Ai for your Enterprise. We demystify the Missing Piece of AI Puzzle - Ontology and how it makes AI domain worthy for Enterprises. 𝐅𝐮𝐥𝐥 𝐛𝐥𝐨𝐠 𝐡𝐞𝐫𝐞. https://lnkd.in/giAwcSAD

“From Data-Backed to Context-Backed Decisions” For too long, we’ve celebrated data-driven decision-making. Dashboards, metrics, and KPIs became the holy grail. But in the real world — enterprise decisions demand reasoning. And reasoning doesn’t come from more data or better dashboards. It comes from understanding cause and effect, context, and intent. That’s the delta — between an AI that simply answers your query, and one that truly understands your business. The missing piece of the AI puzzle? AI Ontology — the system that connects data, relationships, and goals to bring context into every decision. At DecisionX AI, 80% of our existence has been about building best in class Ontology/ Context aware tech. Huge respect for Palantir for what they have done around it. #DecisionIntelligence #AIReasoning #ContextAwareAI #OntologyAI #DecisionXAI

Operationalizing AI models at scale poses significant challenges, particularly due to the often-overlooked costs associated with inference. I found it interesting that while training large language models attracts the spotlight, it's the inference stage that truly drives up expenses during production. How is your organization addressing the complexities of AI inference at scale?

New inspiring AI insights from our colleague, Branislav Popović, AI & ML Expert and Principal Research Fellow! Learn how the model context protocol enhances AI’s strategic agility through context-aware orchestration, and why choosing the right client, balancing performance trade-offs, and ensuring strong governance are essential for effectively deploying adaptive, intelligent AI systems. Find out more here: https://lnkd.in/dtwmJNjw

RAG is a more cost-effective approach to introducing new data to the LLM. It makes generative artificial intelligence (generative AI) technology more broadly accessible and usable. #RAGModel #GenerativeAI #LLMmodel #contentbased #AugumentedGenerativeAI

The article discusses the rapid advancement of AI models and the emerging need for multi-agent systems that can collaborate to tackle complex tasks more effectively. I found it interesting that as developers recognize the limitations of singular models, they're increasingly turning to systems of specialized agents. This shift opens new possibilities for enhancing productivity and innovation in various fields. What are your thoughts on the role of multi-agent systems in the future of AI development?

Everyone talks about AI models, GPUs, and generative breakthroughs. The real bottleneck isn't the model. It's the data. Siloed systems, inconsistent labels, and weak governance are the quiet blockers that stall AI projects before they even begin. Learn more about how these problems undermine AI adoption and how to avoid them at the link below! https://lnkd.in/ee6sMq42 #AI #DataScience #DataEngineering

2026 Belongs to AI Meaning Builders, Not Model Builders - For the better part of a decade, enterprises have been racing to build bigger models and gather more data, believing scale alone would unlock artificial intelligence at full capacity. Yet despite remarkable breakthroughs in generative AI, most organizations still find themselves stuck at the same frustrating juncture: the last mile between technical capabilities and accurate outputs that agentic systems can be built off of. Models horsepower can be 10X but if it can’t perform at high accuracy, it’s doomed to a life of shelfware. The reason is no longer a mystery. The bottleneck to enterprise AI isn’t data or compute […] - https://lnkd.in/evhncAcU